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Information Systems
Decision Support and
Artificial Intelligence
Chapter 4
Chapter Overview
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Types of Decisions
Decision Support
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Decision Support Systems (DSS)
Collaboration Systems
Geographic Information Systems (GIS)
Artificial Intelligence (AI)
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Expert Systems
Neural Networks
Genetic Algorithms
Intelligent Agents
Four phases of decision making
Types of Decisions
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Structured
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Semi-structured
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Unstructured
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Recurring vs. ad-hoc
Decision Support Systems
(DSS)
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Highly flexible and interactive IT system
that is designed to support unstructured
and semi-structured decision making.
DSS Components
DSS Components
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Model management - consists of both the
DSS models and the DSS model
management system.
Data management - performs the function of
storing and maintaining the information that
you want your DSS to use.
User interface management - allows you to
communicate with the DSS.
DSS Capabilities
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Sensitivity Analysis
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What-if Analysis
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the study of the effect that changes in one or more
parts of a model have on other parts of the model
checks the impact of a change in the assumptions
or other input data on the proposed solution
Goal-seeking Analysis
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find the value of the inputs necessary to achieve a
desired level of output
DSS Process
DSS Examples
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General Accident Insurance: Customer buying patterns
and fraud detection
Bank of America: Customer profiles
Frito-Lay, Inc.: Price, advertising, and promotion
selection
Burlington Coat Factory:Store location and inventory mix
Keycorp: Targeting direct mail marketing customers
National Gypsum: Corporate planning & forecasting
Southern Railway: Train dispatching and routing
Texas Oil & Gas: Evaluation of potential drilling sites
United Airlines: Flight scheduling, passenger demand
forecasting
Collaboration Systems
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Interactive computer-based system that
facilitates the solution of semi structured and
unstructured problems by a group of decision
makers.
Tools include - Electronic questionnaires,
Electronic brainstorming tools, Idea
organizers, Questionnaire tools
Collaboration System
Benefits
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Improved preplanning
Increased participation
Open, collaborative meeting atmosphere
Criticism-free idea generation
Evaluation objectivity
Idea organization and evaluation
Setting priorities and making decisions
Documentation of meetings
Access to external information
Geographic Information
Systems (GIS)
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Computer system with software that can
analyze and display data using digitized
maps. Enables display and analysis of
spatial information.
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Examples – Location analysis, law
enforcement, identifying efficient delivery
routes
Artificial Intelligence Systems
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Artificial Intelligence (AI)
– Branch of computer science that deals with
ways of representing knowledge, using
symbols rather than numbers, and
heuristics, or rules of thumb, rather than
algorithms for processing information
– Objectives:
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Make machines smarter
Understand what intelligence is
Make machines more useful
Commercial AI Systems
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Expert systems (ESs)
Neural Networks
Genetic Algorithms
Intelligent Agents
Natural language technology
Speech (voice) understanding
Computer vision and scene recognition
Intelligent computer-aided instruction
Handwriting recognizers
IBM Watson
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90 IBM POWER 750 Servers on 10 racks
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Each server has 4 processors
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Each processor has 8 cores (2880 total cores)
16 Terabytes of RAM and 4 Terabytes of
Clustered Storage
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A single CPU machine would take 2 hours to
answer a single question
Expert Systems
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AI based Information System that applies
reasoning capabilities to solve very specific
problems. These systems utilize expert
knowledge and can replace an expert in the
decision making process.
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Good for diagnostic (what’s wrong?) and
prescriptive (what to do?) problems
Expert Systems Process
Expert Systems Components
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Knowledge base
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Knowledge acquisition
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Inference engine
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User interface
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Explanation module
Inference Engine
Expert Systems People
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Domain expert - provides the domain
expertise in the form of problem-solving
strategies.
Knowledge engineer – IT specialist who
formulates the domain expertise into an
expert system.
Knowledge worker or user - that’s you.
Expert Systems
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An expert system can:
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Reduce errors
Improve customer service
Reduce costs
An expert system can’t:
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Use common sense
Automate all processes
Artificial Neural Network
(ANN)
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Emulates a biological neural network
Receives information from other neurons
or from external sources, transform the
information, and pass it on to other
neurons or as external outputs
Useful for pattern recognition, learning,
and the interpretation of incomplete inputs
Neural Networks
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Self-organizing neural network finds patterns and relationships in
vast amounts of data by itself
Back-propagation neural network a neural network trained by
someone
Neural Networks
Figure 10-15
Genetic Algorithms
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AI based system that mimics the evolutionary,
survival-of-the-fittest process to generate
increasingly better solutions to a problem.
Three genetic concepts:
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Selection - survival of the fittest.
Crossover - combining portions of good outcomes
in the hope of creating an even better outcome.
Mutation - randomly trying combinations and
evaluating the success (or failure) of the outcome.
Intelligent Agents (IA)
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Software that assists you, or acts on
your behalf, in performing repetitive
computer-related tasks.
Four types of intelligent agents include:
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Buyer agents or shopping bots
User or personal agents
Monitoring-and-surveillance or predictive agents
Data-mining agents
Intelligent Agents (IA)
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Autonomy - act without your telling them
every step to take.
Adaptivity - discovering, learning, and
taking action independently.
Sociability - conferring with other
agents.